Visualisation of heterogeneous data with simultaneous feature saliency using Generalised Generative Topographic Mapping

Abstract

Most machine-learning algorithms are designed for datasets with features of a single type whereas very little attention has been given to datasets with mixed-type features. We recently proposed a model to handle mixed types with a probabilistic latent variable formalism. This proposed model describes the data by type-specific distributions that are conditionally independent given the latent space and is called generalised generative topographic mapping (GGTM). It has often been observed that visualisations of high-dimensional datasets can be poor in the presence of noisy features. In this paper we therefore propose to extend the GGTM to estimate feature saliency values (GGTMFS) as an integrated part of the parameter learning process with an expectation-maximisation (EM) algorithm. The efficacy of the proposed GGTMFS model is demonstrated both for synthetic and real datasets.

Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © the authors
Event Title: Workshop new challenges in neural computation 2015
Event Type: Other
Event Location: Informatikzentrum of RWTH Aachen
Event Dates: 2015-10-10 - 2015-10-10
Uncontrolled Keywords: Computer Vision and Pattern Recognition
Full Text Link: http://www.tech ... mlr_03_2015.pdf
Related URLs:
PURE Output Type: Conference contribution
Published Date: 2015-10-01
Authors: Mumtaz, Shahzad
Randrianandrasana, Michel F. (ORCID Profile 0000-0002-4181-1323)
Bassi, Gurjinder
Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)

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Version: Accepted Version


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